Prediksi Curah Hujan Menggunakan Long Short Term Memory

Badriyah, Jamilatul; Fariza, Arna; Harsono, Tri

doi:10.30865/mib.v6i3.4008

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…Teknik ini telah terbukti mampu mengolah volume besar data historis dan faktor-faktor pasar dalam rangka memprediksi pergerakan harga saham di masa mendatang. Dalam konteks ini, tiga teknik machine learning yang sering digunakan adalah Support Vector Machine (SVM), Neural Network (NN), dan K-Nearest Neighbors (KNN) [4], [5].…”

Section: Abstrak -Penelitian Ini Bertujuan Untuk Menganalisis Kinerja...unclassified

Perbandingan Performa Model Machine Learning Support Vector Machine, Neural Network, Dan K-Nearest Neighbors Dalam Prediksi Harga Saham

Sudriyanto,

Syahro,

Fitriani

2023

JARS

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This study aims to analyze the performance of three prediction models, namely K-Nearest Neighbors (K-NN), Neural Network (NN), and Support Vector Machine (SVM), in predicting the stock price of PT Astra International Tbk (ASII.JK). The research encompasses the initial stages through evaluation using optimal parameters for these three algorithms. The research findings reveal that the K-NN prediction model has the lowest Root Mean Square Error (RMSE) value, with a value of 0.037, indicating the most accurate prediction compared to the other models. Despite the NN model having an RMSE of 0.048, which is higher than K-NN, it still provides reasonably accurate predictions. Meanwhile, the SVM model has an RMSE of 0.075, indicating a higher level of error in its predictions. Based on these results, the recommendation is to utilize the K-NN model as the preferred choice for predicting the ASII.JK stock price.

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Section: Abstrak -Penelitian Ini Bertujuan Untuk Menganalisis Kinerja...unclassified

Perbandingan Performa Model Machine Learning Support Vector Machine, Neural Network, Dan K-Nearest Neighbors Dalam Prediksi Harga Saham

Sudriyanto,

Syahro,

Fitriani

2023

JARS

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“…Metrik evaluasi yang digunakan pada penelitian ini adalah MAE (Mean Absolute Error) dan MAPE (Mean Absolute Percentage Error). MAE menghitung rata-rata absolut dari kesalahan prediksi yang tidak terlalu terpengaruhi oleh data outlier [11]. Sedangkan MAPE merupakan nilai rata-rata persentase kesalahan atau error dari beberapa periode [12].…”

Section: Long Short Term Memory (Lstm)unclassified

Prediksi Curah Hujan Di Kabupaten Badung, Bali Menggunakan Metode Long Short-Term Memory

Dharma Saputra,

Hiryanto,

Handhayani

2023

jiksi

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Rainfall is the height of rainwater that falls on a flat area, assuming it doesn't evaporate, doesn't seep, and doesn't flow. Rain levels are measured in mm (millimeters). The target of the research being conducted is in Badung Regency, Bali because Bali is a tourist area that is often visited by tourists and from Indonesian itself, so predictions of meteorology, such as rainfall will greatly impact tourism. In this test, predictions use the Long Short Term Memory (LSTM) method, using daily weather data from the BMKG from 2010 to 2020 as training data and daily weather data for 2021 as prediction data. Based on the test results above, the results show that the two LSTM tests with LSTM Model 128.64 and LSTM Model 64.32 have low MAE and MAPE error values. From First Scenario, the Mean Absolute Error (MAE) value is 8.97246598930908 and Mean Absolute Percentage Error (MAPE) value is 1.7657206683278308%. From Second Scenario, the Mean Absolute Error is 9.706669940783014 and Mean Absolute Percentage Error is 1.9028466692362323%. From the MAE and MAPE values obtained in these two scenarios, it can be proven that from the evaluation results of Rainfall predictions in Badung Regency, Bali, the predictions can be said to be very accurate because they have an error value of less than 10.

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“…Besarnya curah hujan yang akan terjadi dapat diprediksi dengan menggunakan data historis curah hujan [15]. Beberapa algoritma machine learning dalam memprediksi curah hujan telah banyak dilakukan pada penelitian sebelumnya.…”

Section: Pendahuluanunclassified

“…Nilai MAPE yang semakin kecil menunjukkan semakin baik peramalannya sesuai kriteria yang ditunjukkan pada Tabel 3 [45]. Perhitungan nilai MAPE menggunakan Persamaan (15).…”

Section: Mapeunclassified

Penerapan Metode Principal Component Analysis (PCA) dan Long Short-Term Memory (LSTM) dalam Memprediksi Prediksi Curah Hujan Harian

Musfiroh

Novitasari

Intan

et al. 2023

bits

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Since the last three years North Luwu has experienced frequent hydrological disasters in the form of floods and landslides. The disaster had a negative impact on the availability of clean water, failed to plant and even tended to reduce the quality of the harvest. Cocoa is one of the leading commodities of North Luwu Regency whose productivity has decreased due to the impact of climate change so that it will affect the sustainability of the local population's income. Therefore, the purpose of this research is to anticipate rainfall that will occur to prevent or reduce the risk of failure and loss. Principal Component Analysis (PCA) Method is used as feature extraction to find out the most influential variables and the Long Short-Term Memory (LSTM) method is used as a prediction method. Future rainfall is predicted using meteorological variables such as pressure, evaporation, maximum temperature, average humidity, and sunshine duration from 1 January 2017 to 30 September 2022. Based on the PCA results, 4 variables are obtained that have the most influence on rainfall, namely: variable evaporation, maximum temperature, average humidity, and length of sunlight. These variables are used as input to predict rainfall using LSTM. In this study using trial parameters, namely the number of hidden, batch size, and learn rate drop period. The best prediction results were obtained for MAPE of 0.0018 with the number of hidden, batch size and learn rate drop periods of 100, 32, and 50 respectively. The prediction results show very heavy rainfall occurring on August 28, 2021 of 101.9734 mm, 21 September 2021 of 108.6528 mm, and 5 April 2022 of 116.5510 mm. In this study PCA was able to increase accuracy in considering all parameters and choosing the most effective.

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Prediksi Curah Hujan Menggunakan Long Short Term Memory

Cited by 3 publications

References 7 publications

Perbandingan Performa Model Machine Learning Support Vector Machine, Neural Network, Dan K-Nearest Neighbors Dalam Prediksi Harga Saham

Perbandingan Performa Model Machine Learning Support Vector Machine, Neural Network, Dan K-Nearest Neighbors Dalam Prediksi Harga Saham

Prediksi Curah Hujan Di Kabupaten Badung, Bali Menggunakan Metode Long Short-Term Memory

Penerapan Metode Principal Component Analysis (PCA) dan Long Short-Term Memory (LSTM) dalam Memprediksi Prediksi Curah Hujan Harian

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